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robertita-cased-finetuned-fact
filevich/robertita-cased-finetuned-fact
robertita-cased-finetuned-fact is a token classification model from filevich. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Aug 27, 2023
- Updated
- Aug 27, 2023
- SHA
- 8d749479
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Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
robertita-cased-finetuned-fact
This model is a fine-tuned version of filevich/robertita-cased on the fact2020 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0402
- Precision: 0.9958
- Recall: 0.9908
- F1: 0.9915
- Accuracy: 0.9908
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 116 | 0.0372 | 0.9947 | 0.9889 | 0.9895 | 0.9889 |
| No log | 2.0 | 232 | 0.0388 | 0.9961 | 0.9903 | 0.9913 | 0.9903 |
| No log | 3.0 | 348 | 0.0402 | 0.9958 | 0.9908 | 0.9915 | 0.9908 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Languages
- es
- Created
- Aug 27, 2023
- Updated
- Aug 27, 2023